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Beschreibung
This practical guide to optimization combines mathematical theory with hands-on coding examples to explore how Python can be used to model problems and obtain the best possible solutions. Presenting a balance of theory and practical applications, it is the ideal resource for upper-undergraduate and graduate students in applied mathematics, data science, business, industrial engineering and operations research, as well as practitioners in related fields. Beginning with an introduction to the concept of optimization, this text presents the key ingredients of an optimization problem and the choices one needs to make when modeling a real-life problem mathematically. Topics covered range from linear and network optimization to convex optimization and optimizations under uncertainty. The book's Python code snippets, alongside more than 50 Jupyter notebooks on the author's GitHub, allow students to put the theory into practice and solve problems inspired by real-life challenges, while numerous exercises sharpen students' understanding of the methods discussed.
This practical guide to optimization combines mathematical theory with hands-on coding examples to explore how Python can be used to model problems and obtain the best possible solutions. Presenting a balance of theory and practical applications, it is the ideal resource for upper-undergraduate and graduate students in applied mathematics, data science, business, industrial engineering and operations research, as well as practitioners in related fields. Beginning with an introduction to the concept of optimization, this text presents the key ingredients of an optimization problem and the choices one needs to make when modeling a real-life problem mathematically. Topics covered range from linear and network optimization to convex optimization and optimizations under uncertainty. The book's Python code snippets, alongside more than 50 Jupyter notebooks on the author's GitHub, allow students to put the theory into practice and solve problems inspired by real-life challenges, while numerous exercises sharpen students' understanding of the methods discussed.
Über den Autor
Krzysztof Postek is Senior Optimization Data Scientist with the Boston Consulting Group in Amsterdam. He received his Ph.D. in Operations Research in 2017 from Tilburg University. After his postdoc at the Technion - Israel Institute of Technology, he spent several years as a faculty member at Erasmus University Rotterdam and Delft University of Technology. His research interests revolve mostly around optimization under uncertainty.
Inhaltsverzeichnis
1. Mathematical optimization; 2. Linear optimization; 3. Mixed-integer linear optimization; 4. Network optimization; 5. Convex optimization; 6. Conic optimization; 7. Accounting for uncertainty: Optimization meets reality; 8. Robust optimization; 9. Stochastic optimization; 10. Two-stage problems; Appendix A. Linear algebra primer; Appendix B. Solutions of selected exercises; List of Tables; List of Figures; Index.
Details
Erscheinungsjahr: 2025
Fachbereich: Allgemeines
Genre: Importe, Mathematik
Rubrik: Naturwissenschaften & Technik
Thema: Lexika
Medium: Taschenbuch
ISBN-13: 9781009493505
ISBN-10: 1009493507
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Postek, Krzysztof
Zocca, Alessandro
Gromicho, Joaquim A. S.
Hersteller: Cambridge University Press
Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de
Maße: 254 x 178 x 19 mm
Von/Mit: Krzysztof Postek (u. a.)
Erscheinungsdatum: 19.05.2025
Gewicht: 0,67 kg
Artikel-ID: 129274486

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